Rigid and non-rigid motion artifact reduction in X-ray CT using attention module

Ko, Youngjun; Moon, Seunghyuk; Baek, Jongduk; Shim, Hyunjung

doi:10.1016/j.media.2020.101883

Cited by 28 publications

(24 citation statements)

References 36 publications

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“…Machine learning has also been used to correct motion artifacts in reconstructed images. For example, Ko et al developed a deep convolutional neural network (CNN) to compensate for both rigid and nonrigid motion artifacts [29]. Similarly, Lossau et al used three CNNs to reduce the metal artifacts created by pacemakers [30].…”

Section: Motion Artifact Removalmentioning

confidence: 99%

Neural Computed Tomography

Gupta¹,

Colvert²,

Contijoch³

2022

Preprint

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Motion during acquisition of a set of projections can lead to significant motion artifacts in computed tomography reconstructions despite fast acquisition of individual views. In cases such as cardiac imaging, motion may be unavoidable and evaluating motion may be of clinical interest. Reconstructing images with reduced motion artifacts has typically been achieved by developing systems with faster gantry rotation or using algorithms which measure and/or estimate the displacements. However, these approaches have had limited success due to both physical constraints as well as the challenge of estimating/measuring non-rigid, temporally varying, and patient-specific motions. We propose a novel reconstruction framework, NeuralCT, to generate time-resolved images free from motion artifacts. Our approaches utilizes a neural implicit approach and does not require estimation or modeling of the underlying motion. Instead, boundaries are represented using a signed distance metric and neural implicit framework. We utilize 'analysis-by-synthesis' to identify a solution consistent with the acquired sinogram as well as spatial and temporal consistency constraints. We illustrate the utility of NeuralCT in three progressively more complex scenarios: translation of a small circle, heartbeat-like change in an ellipse's diameter, and complex topological deformation. Without hyperparameter tuning or change to the architecture, NeuralCT provides high quality image reconstruction for all three motions, as compared to filtered backprojection using mean-square-error and Dice metrics. Code and supplemental movies are available at https://kunalmgupta.github.io/projects/NeuralCT.html.

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Section: Motion Artifact Removalmentioning

confidence: 99%

Neural Computed Tomography

Gupta¹,

Colvert²,

Contijoch³

2022

Preprint

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“…Substituting (11) in (9) and expanding the observed image moments in terms of the original image moments, it is clear that the zeroth and first invariant moments could be found directly (m…”

Section: Motion Blur Invariant In Moment Domainmentioning

confidence: 99%

“…Some filters such as Gaussian filter can significantly suppress speckles and enhance contrast, but these algorithms cause blurry artefacts at edges and borders of image. There are several advanced approaches to solve this concern about effect of blur [9][10][11] . As ultrasound data are prone to blur and speckle, classical image algorithms may generate lossy information of the deblurred image from an original degraded image based on its boundaries and edges.…”

Section: Introductionmentioning

confidence: 99%

Motion Blur Invariant for Estimating Motion Parameters of Medical Ultrasound Images

Asli

Zhao

Erkoyuncu

2021

Preprint

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The quality of fetal ultrasound images is significantly affected by motion blur while the imaging system requires low motionquality to capture accurate data. This can be achieved with a mathematical model of motion blur in time or frequency domain.We propose a new model of linear motion blur in both frequency and moment domain to analyze the invariant features of blurconvolution for ultrasound images. Moreover, the model also helps to provide an estimation of motion parameters for blurlength and angle. These outcomes might imply great potential of this invariant method in ultrasound imaging application.

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“…Some filters such as Gaussian filter can significantly suppress speckles and enhance contrast, but these algorithms cause blurry artefacts at edges and borders of image. There are several advanced approaches to solve this concern about the effect of blur 9 – 11 . As ultrasound data are prone to blur and speckle, classical image algorithms may generate lossy information of the deblurred image from an original degraded image based on its boundaries and edges.…”

Section: Introductionmentioning

confidence: 99%

Motion blur invariant for estimating motion parameters of medical ultrasound images

Asli

Zhao

Erkoyuncu

2021

Sci Rep

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High-quality medical ultrasound imaging is definitely concerning motion blur, while medical image analysis requires motionless and accurate data acquired by sonographers. The main idea of this paper is to establish some motion blur invariant in both frequency and moment domain to estimate the motion parameters of ultrasound images. We propose a discrete model of point spread function of motion blur convolution based on the Dirac delta function to simplify the analysis of motion invariant in frequency and moment domain. This model paves the way for estimating the motion angle and length in terms of the proposed invariant features. In this research, the performance of the proposed schemes is compared with other state-of-the-art existing methods of image deblurring. The experimental study performs using fetal phantom images and clinical fetal ultrasound images as well as breast scans. Moreover, to validate the accuracy of the proposed experimental framework, we apply two image quality assessment methods as no-reference and full-reference to show the robustness of the proposed algorithms compared to the well-known approaches.

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Rigid and non-rigid motion artifact reduction in X-ray CT using attention module

Cited by 28 publications

References 36 publications

Neural Computed Tomography

Neural Computed Tomography

Motion Blur Invariant for Estimating Motion Parameters of Medical Ultrasound Images

Motion blur invariant for estimating motion parameters of medical ultrasound images

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